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    <abstract>End-to-end multilingual entity linking (MEL) is concerned with identifying multilingual entity mentions and their corresponding entity IDs in a knowledge base. Existing works assumed that entity mentions were given and skipped the entity mention detection step due to a lack of high-quality multilingual training corpora. To overcome this limitation, we propose mReFinED, the first end-to-end multilingual entity linking. Additionally, we propose a bootstrapping mention detection framework that enhances the quality of training corpora. Our experimental results demonstrated that mReFinED outperformed the best existing work in the end-to-end MEL task while being 44 times faster.</abstract>
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%0 Conference Proceedings
%T mReFinED: An Efficient End-to-End Multilingual Entity Linking System
%A Limkonchotiwat, Peerat
%A Cheng, Weiwei
%A Christodoulopoulos, Christos
%A Saffari, Amir
%A Lehmann, Jens
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F limkonchotiwat-etal-2023-mrefined
%X End-to-end multilingual entity linking (MEL) is concerned with identifying multilingual entity mentions and their corresponding entity IDs in a knowledge base. Existing works assumed that entity mentions were given and skipped the entity mention detection step due to a lack of high-quality multilingual training corpora. To overcome this limitation, we propose mReFinED, the first end-to-end multilingual entity linking. Additionally, we propose a bootstrapping mention detection framework that enhances the quality of training corpora. Our experimental results demonstrated that mReFinED outperformed the best existing work in the end-to-end MEL task while being 44 times faster.
%R 10.18653/v1/2023.findings-emnlp.1007
%U https://aclanthology.org/2023.findings-emnlp.1007
%U https://doi.org/10.18653/v1/2023.findings-emnlp.1007
%P 15080-15089

Markdown (Informal)

[mReFinED: An Efficient End-to-End Multilingual Entity Linking System](https://aclanthology.org/2023.findings-emnlp.1007) (Limkonchotiwat et al., Findings 2023)

• mReFinED: An Efficient End-to-End Multilingual Entity Linking System (Limkonchotiwat et al., Findings 2023)

ACL

• Peerat Limkonchotiwat, Weiwei Cheng, Christos Christodoulopoulos, Amir Saffari, and Jens Lehmann. 2023. mReFinED: An Efficient End-to-End Multilingual Entity Linking System. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 15080–15089, Singapore. Association for Computational Linguistics.